Estimating relative abundances of proteins from shotgun proteomics data
1 Department of Genome Sciences, University of Washington, Seattle, WA, USA
2 Department of Otalaryngology–HNS, Seattle, WA, USA
3 Department of Physiology & Biophysics, University of Washington, Seattle, WA, USA
4 Department of Computer Science and Engineering, Seattle, WA, USA
Citation and License
BMC Bioinformatics 2012, 13:308 doi:10.1186/1471-2105-13-308Published: 19 November 2012
Spectral counting methods provide an easy means of identifying proteins with differing abundances between complex mixtures using shotgun proteomics data. The crux spectral-counts command, implemented as part of the Crux software toolkit, implements four previously reported spectral counting methods, the spectral index (SIN), the exponentially modified protein abundance index (emPAI), the normalized spectral abundance factor (NSAF), and the distributed normalized spectral abundance factor (dNSAF).
We compared the reproducibility and the linearity relative to each protein’s abundance of the four spectral counting metrics. Our analysis suggests that NSAF yields the most reproducible counts across technical and biological replicates, and both SIN and NSAF achieve the best linearity.
With the crux spectral-counts command, Crux provides open-source modular methods to analyze mass spectrometry data for identifying and now quantifying peptides and proteins. The C++ source code, compiled binaries, spectra and sequence databases are available at http://noble.gs.washington.edu/proj/crux-spectral-counts webcite.